Streamlined Engineering Workflows for Creating, Calibrating & Optimizing Mega Surface Network Model: A Detailed KOC North Kuwait Case Study

Abstract An up-to-date and calibrated surface network model can offer valuable insights into network bottlenecks and suggest optimal choke and artificial lift parameters. This paper aims to develop a digital twin of a large onshore oil asset, featuring a network model that updates daily with current well and network configurations (including new wells, current well statuses, re-routing, etc.) and utilizes real-time data for automatic calibration of the choke and surface network. Every day, surface network data from KOC corporate databases is integrated into the KwIDF database using ETLs (Extract, Transform & Load) to make it simulator ready. Real-time data for wells, manifolds, and facilities is sourced from KOC's real-time databases. The surface network's framework is manually constructed using GIS (Geographic Information System) maps for length and diameter and stored in a repository. An integration tool employs specialized workflows like AIVGAC (Automatic IPR VLP Generation and Conversion) to convert calibrated well models into IPR/VLP curves. The ANMB (Automatic Network Model Builder) workflow updates the surface network and real-time data in the model, while AIVWMV (Automatic IPR VLP Well Model Validation) ensures the quality of well models. ACC (Automatic Choke Calibration) handles choke calibration, and ANMC (Automatic Network Model Calibration) fine-tunes flowline connections. The HR (Hourly Run) workflow runs the model hourly, providing virtual rates in the surface network, and the ACEO (Automatic Choke & ESP Optimization) workflow delivers optimal choke and artificial lift parameters. The GC summary page showcases individual GC results, highlighting model outcomes and input data quality. The Network Heat Map page visualizes active pipeline connections and displays parameters like phase rates, pipeline data, and real-time data. It also generates specific alarms and alerts, such as high-pressure loss index, LP to HP and vice versa header switching, no/low flow, and real-time data integrity based on real-time and model data. The ACEO dashboard lists wells with their optimal choke settings and ESP frequency. Additionally, the Network KPI page presents the quality of real-time and well test data used in the models. This paper highlights the crucial role of a digital twin in automating daily updates, calibrations, and recommendations to optimize production efficiently.